Feature Engineering and Creation

Imports and Setup

In [1]:
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
from collections import Counter
from sklearn.decomposition import TruncatedSVD, PCA
from sklearn.feature_selection import SelectKBest

from sklearn.preprocessing import StandardScaler, OrdinalEncoder

sns.set(style='darkgrid', palette='pastel')
pd.options.display.max_columns = None
In [2]:
data = pd.read_csv('../data/processed/0_cleaned.csv')
data_backup = data.copy()

label = data[['Value', 'Wage']]
data.drop(columns=['Value', 'Wage'], inplace=True)
In [3]:
data.head(5)
Out[3]:
Age Nationality Overall Potential Club Special Preferred Foot International Reputation Weak Foot Skill Moves Work Rate Body Type Position Jersey Number Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes
0 31.0 Argentina 94.0 94.0 FC Barcelona 2202.0 Left 5.0 4.0 4.0 Medium/ Medium Messi RF 10.0 170.18 159.0 88.0 88.0 88.0 92.0 93.0 93.0 93.0 92.0 93.0 93.0 93.0 91.0 84.0 84.0 84.0 91.0 64.0 61.0 61.0 61.0 64.0 59.0 47.0 47.0 47.0 59.0 84.0 95.0 70.0 90.0 86.0 97.0 93.0 94.0 87.0 96.0 91.0 86.0 91.0 95.0 95.0 85.0 68.0 72.0 59.0 94.0 48.0 22.0 94.0 94.0 75.0 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0
1 33.0 Portugal 94.0 94.0 Juventus 2228.0 Right 5.0 4.0 5.0 High/ Low C. Ronaldo ST 7.0 187.96 183.0 91.0 91.0 91.0 89.0 90.0 90.0 90.0 89.0 88.0 88.0 88.0 88.0 81.0 81.0 81.0 88.0 65.0 61.0 61.0 61.0 65.0 61.0 53.0 53.0 53.0 61.0 84.0 94.0 89.0 81.0 87.0 88.0 81.0 76.0 77.0 94.0 89.0 91.0 87.0 96.0 70.0 95.0 95.0 88.0 79.0 93.0 63.0 29.0 95.0 82.0 85.0 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0
2 26.0 Brazil 92.0 93.0 Paris Saint-Germain 2143.0 Right 5.0 5.0 5.0 High/ Medium Neymar LW 10.0 175.26 150.0 84.0 84.0 84.0 89.0 89.0 89.0 89.0 89.0 89.0 89.0 89.0 88.0 81.0 81.0 81.0 88.0 65.0 60.0 60.0 60.0 65.0 60.0 47.0 47.0 47.0 60.0 79.0 87.0 62.0 84.0 84.0 96.0 88.0 87.0 78.0 95.0 94.0 90.0 96.0 94.0 84.0 80.0 61.0 81.0 49.0 82.0 56.0 36.0 89.0 87.0 81.0 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0
3 27.0 Spain 91.0 93.0 Manchester United 1471.0 Right 4.0 3.0 1.0 Medium/ Medium Lean GK 1.0 193.04 168.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17.0 13.0 21.0 50.0 13.0 18.0 21.0 19.0 51.0 42.0 57.0 58.0 60.0 90.0 43.0 31.0 67.0 43.0 64.0 12.0 38.0 30.0 12.0 68.0 40.0 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0
4 27.0 Belgium 91.0 92.0 Manchester City 2281.0 Right 4.0 5.0 4.0 High/ High Normal RCM 7.0 180.34 154.0 82.0 82.0 82.0 87.0 87.0 87.0 87.0 87.0 88.0 88.0 88.0 88.0 87.0 87.0 87.0 88.0 77.0 77.0 77.0 77.0 77.0 73.0 66.0 66.0 66.0 73.0 93.0 82.0 55.0 92.0 82.0 86.0 85.0 83.0 91.0 91.0 78.0 76.0 79.0 91.0 77.0 91.0 63.0 90.0 75.0 91.0 76.0 61.0 87.0 94.0 79.0 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0
In [4]:
label.isnull().sum()
Out[4]:
Value    0
Wage     0
dtype: int64

Transform Categorical Features

In [20]:
categorical_columns = np.asarray([not np.issubdtype(data[col].dtype, np.number) for col in data.columns], dtype=np.bool)
cat_col_names = [col for col in data.columns if not np.issubdtype(data[col].dtype, np.number)]
num_col_names = [col for col in data.columns if np.issubdtype(data[col].dtype, np.number)]

categorical_data = data.iloc[:, categorical_columns]
non_categorical = data.iloc[:, ~categorical_columns]
categorical_data.reset_index(inplace=True, drop=True)
non_categorical.reset_index(inplace=True, drop=True)

Transform Using OneHotEncoding

In [47]:
cardinality_threshold = 10
cols_to_reduce_dim = []
for c in cat_col_names:
    levels = categorical_data[c].drop_duplicates().shape[0]
    if levels > cardinality_threshold:
        cols_to_reduce_dim.append(c)      

df_reduce = categorical_data[cols_to_reduce_dim]
df_reduce = pd.get_dummies(df_reduce)

svd = PCA(n_components=0.85)
df_reduce = svd.fit_transform(df_reduce)

column_names = []
for col in range(df_reduce.shape[1]):
    column_names.append('reduced_col_{}'.format(col))
df_reduce = pd.DataFrame(df_reduce, columns=column_names)

df_dummies = categorical_data.drop(cols_to_reduce_dim, axis=1)
df_dummies = pd.get_dummies(df_dummies)

df_dummies = df_dummies.join(df_reduce)
df_dummies = df_dummies.join(non_categorical)

Transform Using Ordinal Encoding

In [48]:
encoder = OrdinalEncoder()
encoder.fit(categorical_data)
column_names = categorical_data.columns
categorical_data = encoder.transform(categorical_data)
df_ordinal = pd.DataFrame(categorical_data, columns=column_names)
del categorical_data
df_ordinal = non_categorical.join(df_ordinal)
del non_categorical

Normalize Data

Normalize Ordinal Encoded Data

In [49]:
scaler = StandardScaler()
column_names = df_ordinal.columns
df_ordinal = scaler.fit_transform(df_ordinal)
df_ordinal = pd.DataFrame(df_ordinal, columns=column_names)
df_ordinal.head()
del column_names

Normalize One Hot Encoded Data

In [50]:
scaler = StandardScaler()
column_names = df_dummies.columns
df_dummies = scaler.fit_transform(df_dummies)
df_dummies = pd.DataFrame(df_dummies, columns=column_names)
df_dummies.head()
del column_names
In [51]:
df_dummies.head()
Out[51]:
Preferred Foot_Left Preferred Foot_Right Work Rate_High/ High Work Rate_High/ Low Work Rate_High/ Medium Work Rate_Low/ High Work Rate_Low/ Low Work Rate_Low/ Medium Work Rate_Medium/ High Work Rate_Medium/ Low Work Rate_Medium/ Medium Body Type_Akinfenwa Body Type_C. Ronaldo Body Type_Courtois Body Type_Lean Body Type_Messi Body Type_Neymar Body Type_Normal Body Type_PLAYER_BODY_TYPE_25 Body Type_Shaqiri Body Type_Stocky reduced_col_0 reduced_col_1 reduced_col_2 reduced_col_3 reduced_col_4 reduced_col_5 reduced_col_6 reduced_col_7 reduced_col_8 reduced_col_9 reduced_col_10 reduced_col_11 reduced_col_12 reduced_col_13 reduced_col_14 reduced_col_15 reduced_col_16 reduced_col_17 reduced_col_18 reduced_col_19 reduced_col_20 reduced_col_21 reduced_col_22 reduced_col_23 reduced_col_24 reduced_col_25 reduced_col_26 reduced_col_27 reduced_col_28 reduced_col_29 reduced_col_30 reduced_col_31 reduced_col_32 reduced_col_33 reduced_col_34 reduced_col_35 reduced_col_36 reduced_col_37 reduced_col_38 reduced_col_39 reduced_col_40 reduced_col_41 reduced_col_42 reduced_col_43 reduced_col_44 reduced_col_45 reduced_col_46 reduced_col_47 reduced_col_48 reduced_col_49 reduced_col_50 reduced_col_51 reduced_col_52 reduced_col_53 reduced_col_54 reduced_col_55 reduced_col_56 reduced_col_57 reduced_col_58 reduced_col_59 reduced_col_60 reduced_col_61 reduced_col_62 reduced_col_63 reduced_col_64 reduced_col_65 reduced_col_66 reduced_col_67 reduced_col_68 reduced_col_69 reduced_col_70 reduced_col_71 reduced_col_72 reduced_col_73 reduced_col_74 reduced_col_75 reduced_col_76 reduced_col_77 reduced_col_78 reduced_col_79 reduced_col_80 reduced_col_81 reduced_col_82 reduced_col_83 reduced_col_84 reduced_col_85 reduced_col_86 reduced_col_87 reduced_col_88 reduced_col_89 reduced_col_90 reduced_col_91 reduced_col_92 reduced_col_93 reduced_col_94 reduced_col_95 reduced_col_96 reduced_col_97 reduced_col_98 reduced_col_99 reduced_col_100 reduced_col_101 reduced_col_102 reduced_col_103 reduced_col_104 reduced_col_105 reduced_col_106 reduced_col_107 reduced_col_108 reduced_col_109 reduced_col_110 reduced_col_111 reduced_col_112 reduced_col_113 reduced_col_114 reduced_col_115 reduced_col_116 reduced_col_117 reduced_col_118 reduced_col_119 reduced_col_120 reduced_col_121 reduced_col_122 reduced_col_123 reduced_col_124 reduced_col_125 reduced_col_126 reduced_col_127 reduced_col_128 reduced_col_129 reduced_col_130 reduced_col_131 reduced_col_132 reduced_col_133 reduced_col_134 reduced_col_135 reduced_col_136 reduced_col_137 reduced_col_138 reduced_col_139 reduced_col_140 reduced_col_141 reduced_col_142 reduced_col_143 reduced_col_144 reduced_col_145 reduced_col_146 reduced_col_147 reduced_col_148 reduced_col_149 reduced_col_150 reduced_col_151 reduced_col_152 reduced_col_153 reduced_col_154 reduced_col_155 reduced_col_156 reduced_col_157 reduced_col_158 reduced_col_159 reduced_col_160 reduced_col_161 reduced_col_162 reduced_col_163 reduced_col_164 reduced_col_165 reduced_col_166 reduced_col_167 reduced_col_168 reduced_col_169 reduced_col_170 reduced_col_171 reduced_col_172 reduced_col_173 reduced_col_174 reduced_col_175 reduced_col_176 reduced_col_177 reduced_col_178 reduced_col_179 reduced_col_180 reduced_col_181 reduced_col_182 reduced_col_183 reduced_col_184 reduced_col_185 reduced_col_186 reduced_col_187 reduced_col_188 reduced_col_189 reduced_col_190 reduced_col_191 reduced_col_192 reduced_col_193 reduced_col_194 reduced_col_195 reduced_col_196 reduced_col_197 reduced_col_198 reduced_col_199 reduced_col_200 reduced_col_201 reduced_col_202 reduced_col_203 reduced_col_204 reduced_col_205 reduced_col_206 reduced_col_207 reduced_col_208 reduced_col_209 reduced_col_210 reduced_col_211 reduced_col_212 reduced_col_213 reduced_col_214 reduced_col_215 reduced_col_216 reduced_col_217 reduced_col_218 reduced_col_219 reduced_col_220 reduced_col_221 reduced_col_222 reduced_col_223 reduced_col_224 reduced_col_225 reduced_col_226 reduced_col_227 reduced_col_228 reduced_col_229 reduced_col_230 reduced_col_231 reduced_col_232 reduced_col_233 reduced_col_234 reduced_col_235 reduced_col_236 reduced_col_237 reduced_col_238 reduced_col_239 reduced_col_240 reduced_col_241 reduced_col_242 reduced_col_243 reduced_col_244 reduced_col_245 reduced_col_246 reduced_col_247 reduced_col_248 reduced_col_249 reduced_col_250 reduced_col_251 reduced_col_252 reduced_col_253 reduced_col_254 reduced_col_255 reduced_col_256 reduced_col_257 reduced_col_258 reduced_col_259 reduced_col_260 reduced_col_261 reduced_col_262 reduced_col_263 reduced_col_264 reduced_col_265 reduced_col_266 reduced_col_267 reduced_col_268 reduced_col_269 reduced_col_270 reduced_col_271 reduced_col_272 reduced_col_273 reduced_col_274 reduced_col_275 reduced_col_276 reduced_col_277 reduced_col_278 reduced_col_279 reduced_col_280 reduced_col_281 reduced_col_282 reduced_col_283 reduced_col_284 reduced_col_285 reduced_col_286 reduced_col_287 reduced_col_288 reduced_col_289 reduced_col_290 reduced_col_291 reduced_col_292 reduced_col_293 reduced_col_294 reduced_col_295 reduced_col_296 reduced_col_297 reduced_col_298 reduced_col_299 reduced_col_300 reduced_col_301 reduced_col_302 reduced_col_303 reduced_col_304 reduced_col_305 reduced_col_306 reduced_col_307 reduced_col_308 reduced_col_309 reduced_col_310 reduced_col_311 reduced_col_312 reduced_col_313 reduced_col_314 reduced_col_315 reduced_col_316 reduced_col_317 reduced_col_318 reduced_col_319 reduced_col_320 reduced_col_321 reduced_col_322 reduced_col_323 reduced_col_324 reduced_col_325 reduced_col_326 reduced_col_327 reduced_col_328 reduced_col_329 reduced_col_330 reduced_col_331 reduced_col_332 reduced_col_333 reduced_col_334 reduced_col_335 reduced_col_336 reduced_col_337 reduced_col_338 reduced_col_339 reduced_col_340 reduced_col_341 reduced_col_342 reduced_col_343 reduced_col_344 reduced_col_345 reduced_col_346 reduced_col_347 reduced_col_348 reduced_col_349 reduced_col_350 reduced_col_351 reduced_col_352 reduced_col_353 reduced_col_354 reduced_col_355 reduced_col_356 reduced_col_357 reduced_col_358 reduced_col_359 reduced_col_360 reduced_col_361 reduced_col_362 reduced_col_363 reduced_col_364 reduced_col_365 reduced_col_366 reduced_col_367 reduced_col_368 reduced_col_369 reduced_col_370 reduced_col_371 reduced_col_372 reduced_col_373 reduced_col_374 reduced_col_375 reduced_col_376 reduced_col_377 reduced_col_378 reduced_col_379 reduced_col_380 reduced_col_381 reduced_col_382 reduced_col_383 reduced_col_384 reduced_col_385 reduced_col_386 reduced_col_387 reduced_col_388 reduced_col_389 reduced_col_390 reduced_col_391 reduced_col_392 reduced_col_393 reduced_col_394 reduced_col_395 reduced_col_396 reduced_col_397 reduced_col_398 reduced_col_399 reduced_col_400 reduced_col_401 reduced_col_402 reduced_col_403 reduced_col_404 reduced_col_405 reduced_col_406 reduced_col_407 reduced_col_408 reduced_col_409 reduced_col_410 reduced_col_411 reduced_col_412 reduced_col_413 reduced_col_414 reduced_col_415 reduced_col_416 reduced_col_417 reduced_col_418 reduced_col_419 reduced_col_420 reduced_col_421 reduced_col_422 reduced_col_423 reduced_col_424 reduced_col_425 reduced_col_426 reduced_col_427 reduced_col_428 reduced_col_429 reduced_col_430 reduced_col_431 reduced_col_432 reduced_col_433 reduced_col_434 reduced_col_435 reduced_col_436 Age Overall Potential Special International Reputation Weak Foot Skill Moves Jersey Number Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes
0 1.819966 -1.819966 -0.243320 -0.200086 -0.460142 -0.157398 -0.043311 -0.159226 -0.320339 -0.221602 0.922535 -0.007421 -0.007421 -0.007421 -0.739256 134.751623 -0.007421 -1.183518 -0.007421 -0.007421 -0.258813 -0.025030 -0.124137 0.043661 -0.489503 0.076626 -0.052710 0.045809 -0.696539 0.254391 -1.116797 -1.076762 0.449501 3.152894 1.795082 0.980016 0.278538 0.393420 0.053375 0.385782 0.127697 0.457118 0.091012 -0.003341 0.100609 -0.026914 -0.062550 -0.038607 0.021641 0.029073 -0.010898 -0.012549 -0.010883 -0.034975 -0.153374 -0.018113 -0.056407 0.063416 0.007848 -0.077980 -0.149507 0.387160 -0.204118 0.021048 0.030567 -0.068259 0.290932 0.124257 -0.175111 0.068023 -0.000238 -0.026643 -0.130464 -0.113250 -0.065391 -0.025833 0.042499 -0.159169 0.293210 0.920934 0.159750 0.043026 -0.080303 0.067361 -0.138927 0.303237 0.429450 0.316550 -0.172149 -0.153590 -0.046946 -0.050547 -0.049805 -0.029740 -0.001275 -0.007572 -0.010982 -0.173994 0.016092 -0.126822 -0.189650 -0.079487 -0.120204 -0.180438 0.253743 -0.493973 0.735913 0.004201 1.103071 -0.022659 -0.204418 -3.121383 -2.655840 12.075566 -11.710633 -0.778650 -12.673207 2.489642 1.884204 0.715373 -4.532138 0.480582 1.045607 2.115204 -3.982130 2.712623 -1.487675 -1.367518 0.599605 -6.291128 0.028271 2.856445 -4.943365 3.262012 0.698759 0.452892 3.169241 0.733207 0.189608 0.014154 -0.535051 -0.108434 0.201775 0.370942 0.469042 0.740614 0.502943 0.955104 0.536674 0.063468 -0.200761 0.358547 0.392161 0.301760 -0.205254 1.360603 -0.295316 0.139560 0.245110 1.115577 0.721629 -0.205635 0.452851 0.520400 1.370042 0.514755 -0.662536 0.561032 -0.023284 0.150546 -0.121182 -0.042828 -0.133688 0.062027 0.014256 0.026487 -0.075329 -0.064654 0.017431 0.042805 0.022480 0.038812 0.030842 -0.006894 -0.015514 0.013093 -0.015921 -0.004244 -0.039522 -0.031782 -0.005264 0.017619 -0.011958 0.030580 0.003526 0.034625 -0.018188 0.001857 0.049740 -0.022147 0.025406 0.002060 0.010423 -0.035769 0.066609 0.062303 0.048421 0.005261 0.006434 -0.001493 0.018563 -0.005977 -0.034769 0.078932 0.042463 -0.069521 0.008345 0.070999 0.064913 0.125622 -0.167970 0.029444 0.049896 -0.027177 -0.079337 -0.037055 0.104196 0.063288 0.099125 -0.213047 -0.108603 -0.012141 -0.155789 -0.137741 -0.113855 0.055748 0.264588 -0.138650 -0.230307 0.025343 -0.014355 0.371855 -0.226583 0.022212 -0.218888 0.097323 -0.539793 -0.186057 0.174866 0.040428 0.411391 -0.193008 -0.312114 0.529564 -0.795016 0.107424 0.376049 0.333085 0.033996 -0.077841 0.056613 -0.071118 0.517264 -0.178085 -0.084891 -0.164599 -0.380301 -0.363339 -0.341733 0.480933 0.255668 -0.144997 0.058395 0.147948 -0.047594 -0.273265 -0.139852 -0.016671 -0.080807 0.155682 -0.267176 0.101779 -0.001658 -0.240250 -0.050075 0.098974 0.064657 -0.005191 -0.210237 0.210051 0.143640 -0.110922 0.127793 -0.228330 -0.250901 -0.115086 -0.031032 0.456749 0.273699 -0.183640 1.058342 0.367576 0.533280 -0.588887 -0.099736 0.258391 -0.694769 -0.584398 0.553087 0.170711 0.132539 -0.534545 -0.458022 0.345254 -0.115451 0.096934 -0.377557 -0.190382 0.270146 -0.024930 0.151907 -0.139731 0.000784 -0.011357 -0.045590 0.014198 0.003449 -0.006683 0.098726 -0.025701 -0.021375 0.007932 -0.007125 -0.007166 0.032126 0.034834 -0.016020 -0.006656 -0.009631 -0.005452 0.101521 -0.001392 -0.035765 -0.040548 -0.012966 -0.026198 0.016646 0.015037 0.018060 0.005419 -0.000264 -0.008489 0.002495 -0.009407 0.009845 -0.008405 -0.005385 0.001375 -0.000913 -0.000753 0.000207 0.000363 -1.363825e-13 -1.448941e-13 -7.334690e-14 -2.202886e-13 -1.315246e-13 3.045389e-14 5.116986e-14 -1.132121e-13 6.418524e-15 2.731428e-14 -2.437712e-14 2.464209e-14 -1.299866e-13 -1.767396e-13 6.222596e-15 -8.049848e-14 1.444140e-13 -9.275325e-14 -1.132351e-13 -1.626771e-13 5.938240e-14 -2.083328e-13 1.282210e-13 1.428713e-13 -1.582860e-13 -6.093101e-13 -1.462930e-13 -1.788843e-13 5.282681e-14 -0.000133 0.000393 -0.000119 0.000785 0.000306 -0.000736 0.004750 -0.005332 -0.002994 0.009795 0.001406 -0.004008 -0.011407 0.033392 -0.036956 0.040971 -0.019653 -0.022804 0.052971 0.038464 0.054940 0.023719 0.058886 -0.014836 -0.045852 -0.057994 -0.051461 0.015118 0.029555 -0.075096 -0.007399 0.086235 -0.020287 -0.163265 -0.057984 -0.181406 -0.097463 -0.130997 0.133144 0.049848 0.067051 0.053665 -0.134891 -0.022085 0.310444 -0.202646 0.064657 0.078541 0.156599 -0.157880 0.059175 0.136121 0.081225 0.126736 -0.045943 0.263086 0.313761 -0.161997 -0.257737 0.750700 -0.087125 -0.008060 0.315903 0.290849 -0.078487 0.158924 -0.068475 1.258441 4.013364 3.697415 2.213984 9.864420 1.593944 2.167171 -0.597693 -1.646010 -0.447583 1.818665 1.818665 1.818665 1.901392 1.973099 1.973099 1.973099 1.901392 1.957564 1.957564 1.957564 1.828702 1.603458 1.603458 1.603458 1.828702 0.646400 0.519988 0.519988 0.519988 0.646400 0.427328 -0.114632 -0.114632 -0.114632 0.427328 1.865922 2.532567 1.018552 2.130287 2.435355 2.201445 2.491426 2.925736 2.237037 2.255198 1.767621 1.452129 1.862187 3.680643 2.195382 1.713704 0.246247 0.552403 -0.502679 2.434582 -0.453087 -1.193365 2.255244 2.869906 1.684414 3.266205 -0.717531 -0.909268 -0.923569 -0.599961 -0.318908 -0.074659 -0.140241 -0.485161
1 -0.549461 0.549461 -0.243320 4.997854 -0.460142 -0.157398 -0.043311 -0.159226 -0.320339 -0.221602 -1.083970 -0.007421 134.751623 -0.007421 -0.739256 -0.007421 -0.007421 -1.183518 -0.007421 -0.007421 -0.258813 2.398178 1.008121 0.682379 -0.138231 0.163670 -0.075588 -0.201962 0.072392 -0.071780 0.036250 -0.205407 -0.066710 -0.124488 -0.237288 -0.449089 -0.198792 -0.230692 -0.100495 -0.520811 -0.308699 0.001603 -0.338568 0.146506 -0.083066 -0.160981 0.125811 0.812519 -0.425463 -0.497097 0.695280 -0.265159 -0.043956 0.224522 -0.483118 1.594094 5.865933 -3.389932 -0.532820 -0.956426 -0.847770 -0.008147 -0.316175 -0.057831 -0.181831 -0.370449 0.207443 0.024829 -0.708669 0.382464 0.101958 0.054491 -0.118635 -0.198574 -0.064656 -0.106369 -0.044582 -0.136223 -0.088453 0.118739 0.081802 0.058662 -0.120243 -0.114975 -0.147305 -0.032455 0.012907 0.010350 -0.070376 -0.125345 -0.100013 0.178665 -0.328442 -0.290382 0.004605 -0.098633 -0.106563 -0.174978 0.003670 -0.171749 -0.030454 -0.160686 -0.088235 -0.049885 -0.146354 0.020687 0.091310 -0.085712 0.191194 -0.036853 -0.227961 -0.158188 -0.116416 0.114118 -0.158092 -0.031987 0.164136 -0.064716 0.075683 -0.058872 0.031380 -0.085117 0.003234 -0.060703 -0.005989 -0.019529 0.132114 -0.138066 -0.043068 -0.048845 0.105723 0.006907 0.005168 0.096942 -0.353603 -0.180423 -0.201193 0.016554 0.043206 -0.001453 -0.022829 0.147152 -0.032314 0.070600 0.136919 -0.069818 -0.016255 0.201512 0.109765 0.006962 -0.058626 0.131766 -0.206312 -0.230839 -0.128875 1.233090 -0.524567 -0.069288 -0.039048 -0.014911 0.167241 0.271031 0.178742 -0.034539 0.449607 0.080966 -0.173549 0.044169 -0.035222 0.062597 -0.051253 -0.038977 -0.049965 -0.016291 -0.002645 -0.104362 -0.011617 0.002160 -0.072223 0.025964 -0.013008 0.002857 0.043406 0.007321 -0.005303 -0.000699 0.013769 0.000660 -0.016724 0.001320 0.000178 -0.000552 -0.028578 -0.005265 0.003328 0.004945 -0.008196 -0.000306 0.000200 0.010084 0.019227 0.010336 0.009096 0.043233 -0.011131 -0.024469 0.010955 0.035454 -0.022837 0.021715 -0.057549 0.025239 0.028902 -0.049782 -0.006342 -0.026603 -0.002942 -0.057084 0.022124 0.003381 -0.057972 -0.072170 -0.071880 0.039197 0.048447 0.150653 0.123678 0.009686 -0.013250 0.056986 -0.036353 -0.152211 0.047230 -0.059988 -0.054894 -0.083391 0.099022 -0.198829 0.035494 0.136022 0.047292 -0.076347 -0.002451 0.138738 0.030963 -0.029348 -0.375283 -0.241350 -0.297130 -0.205267 0.262311 0.002931 -0.148200 0.416982 -0.435787 0.010200 0.164588 0.326081 -0.142780 -0.149021 0.071213 -0.087520 0.018125 -0.111020 0.044942 -0.024689 -0.180849 -0.174874 -0.224991 -0.183709 0.010069 -0.119549 -0.092680 0.007332 -0.108653 -0.246026 -0.051974 -0.093387 -0.005088 0.179644 -0.122082 0.094056 -0.111098 0.016984 -0.028037 0.027459 -0.128117 -0.126827 -0.004236 0.020453 0.117548 -0.493684 0.380802 -0.356318 -0.345527 -0.293632 0.021088 -0.226347 -0.131183 -0.227184 0.533804 0.600246 -0.011850 -0.359462 0.067918 0.426015 0.529943 -0.383941 0.102393 0.103441 -0.109117 -0.295097 -0.182595 0.147071 0.016338 -0.119312 0.058241 -0.150590 0.063461 0.064448 -0.166495 0.058106 -0.077925 -0.065756 0.021276 0.108737 0.030786 -0.007117 0.022572 -0.021833 -0.036456 -0.020514 0.008522 0.001721 0.007231 -0.038603 0.043413 -0.062598 0.049743 0.048378 0.043156 0.026021 -0.013640 0.015648 0.018627 -0.033522 0.023908 0.010241 0.000658 0.027045 0.006543 0.004898 0.000056 -0.001713 0.019970 -0.005259 -0.017651 0.001101 -0.000428 -0.001169 -0.001682 0.001803 8.253140e-13 3.036098e-12 -1.282231e-14 4.822722e-13 -5.724605e-13 -8.322810e-14 3.717329e-14 -4.263749e-14 -1.219619e-12 6.586211e-13 1.358917e-12 -4.520304e-13 -1.495935e-13 -3.053587e-13 6.970258e-13 -6.801523e-13 -7.709663e-13 -6.007023e-14 -8.788278e-14 3.634391e-13 3.427984e-13 1.478694e-14 4.376906e-13 -2.124123e-13 -1.441653e-13 2.924280e-13 -3.475302e-13 2.044947e-13 5.594822e-13 0.000646 0.001351 0.003261 -0.000449 0.000830 0.008345 -0.000068 0.003175 0.016376 -0.007787 0.003761 -0.005638 -0.011902 0.001815 -0.034904 0.048006 0.075591 0.090641 -0.006339 -0.043793 0.021340 -0.029072 0.044513 0.013373 -0.083284 -0.077810 0.052233 0.008271 0.021237 -0.042025 0.198054 0.113263 -0.058153 0.094471 0.228296 -0.176797 -0.122531 0.204857 -0.378788 -0.199944 -0.363128 -0.012757 0.139071 -0.075139 0.253585 -0.090229 -0.217825 0.218308 -0.267917 0.199734 0.094606 0.074341 0.103974 -0.187454 -0.243285 0.170159 0.468823 0.124462 -0.274712 0.274267 -0.545699 0.352986 -0.046200 0.191029 0.390720 0.107193 -0.183584 1.686666 4.013364 3.697415 2.309273 9.864420 1.593944 3.489672 -0.785781 0.995907 1.091577 1.967451 1.967451 1.967451 1.757292 1.828258 1.828258 1.828258 1.757292 1.716682 1.716682 1.716682 1.684202 1.454577 1.454577 1.454577 1.684202 0.696360 0.519988 0.519988 0.519988 0.696360 0.527106 0.174842 0.174842 0.174842 0.527106 1.865922 2.481351 2.111799 1.518005 2.491871 1.725503 1.839066 1.895887 1.584613 2.135338 1.633639 1.793436 1.591288 3.791628 0.426820 2.293836 2.530565 1.559053 1.090102 2.382660 0.410595 -0.855140 2.306451 2.021639 2.321210 3.178760 -0.968738 -0.770785 -1.064489 -0.543447 -0.318908 -0.074659 -0.140241 -0.318073
2 -0.549461 0.549461 -0.243320 -0.200086 2.173241 -0.157398 -0.043311 -0.159226 -0.320339 -0.221602 -1.083970 -0.007421 -0.007421 -0.007421 -0.739256 -0.007421 134.751623 -1.183518 -0.007421 -0.007421 -0.258813 -0.017085 -0.155351 -0.099347 -0.561520 -0.133703 0.008739 -0.211100 -0.385805 0.249944 -1.098549 0.653056 -0.692954 -0.125449 -2.555751 3.164321 -0.052203 1.098404 -0.196219 0.460858 -0.464040 2.615813 0.221279 1.488161 0.097318 1.035531 -0.640831 3.362325 -3.844559 1.125514 -1.521700 -0.723552 -0.176947 -0.775217 -0.578675 1.033989 -0.951941 0.369220 0.133379 0.098876 0.149616 -1.490444 0.194185 -0.329183 -0.214546 0.224300 -0.307572 -0.026110 -0.131600 0.223520 0.090770 -0.076821 -0.052729 -0.052196 -0.119112 0.116269 0.024458 -0.026558 -0.126343 -0.197976 0.023151 -0.260938 -0.042340 0.072105 -0.029533 0.036162 -0.180051 -0.064309 0.075104 -0.106197 -0.151694 -0.231420 -0.106367 -0.031178 -0.009255 -0.065675 -0.127407 -0.191857 0.027734 -0.283441 -0.068381 -0.126702 -0.129831 -0.111300 -0.417849 0.155636 0.311076 -0.300572 0.016992 0.274355 -0.040008 -0.238912 -0.166051 -0.254214 -0.041283 -0.098426 -0.131329 -0.326457 -0.156098 -0.011257 -0.017541 0.021224 0.127563 0.101303 -0.037059 -0.154341 -0.051848 0.262180 -0.169659 0.210952 0.187486 -0.827832 -0.006134 -0.083404 0.271356 -0.557108 -0.521545 0.000665 0.227176 -0.078251 0.183786 -0.100993 0.242239 0.222050 0.177046 -0.533937 -0.061600 0.304169 -0.280282 -0.089176 -0.057149 0.034380 -0.057564 -0.091304 0.277372 -1.440545 -0.086590 0.372596 -0.440936 0.043286 -0.142858 -0.788251 -1.560627 -1.358089 0.460762 -0.257737 0.111929 0.540291 -1.094658 -0.444038 -0.087696 -1.772570 0.605983 0.818940 -2.372275 0.689227 0.677397 -1.374102 -3.337659 -5.023191 0.844339 0.982759 0.601445 1.100664 1.813789 -1.714559 -0.090288 1.538776 1.998732 2.086108 -0.273292 -1.747413 1.990532 -0.363398 1.175752 3.586258 0.749065 3.728046 0.228683 0.934633 0.215514 -2.508128 -1.804746 4.427921 0.118978 0.055027 1.158042 -1.499516 3.079435 -0.775953 -1.433359 -0.246414 -3.017052 1.986929 1.096578 0.747984 2.275328 -5.606073 3.671009 1.489590 -1.241723 -3.749192 -2.134990 8.933014 6.567214 0.032593 -3.171463 -0.538306 -4.845864 -1.255298 -5.763620 4.106369 -7.231835 -3.165886 -3.668339 0.931353 -3.003468 -4.123856 -0.071417 -2.660184 -0.241473 2.695922 -0.391085 -0.309550 -0.217746 -0.244788 0.045692 -0.207735 -0.365621 0.315363 -1.203033 -0.468928 -0.408268 -0.420352 -0.330091 -0.207700 0.981062 0.319396 0.419477 -0.162150 -0.460295 -0.235526 0.110202 0.727480 0.080552 -0.661481 -0.787856 -0.150934 -0.035326 0.083032 0.177802 -0.142434 0.170785 0.038726 0.176184 -0.057977 0.195241 0.240600 0.194533 -0.047749 0.013702 -0.099494 0.043197 0.008421 0.071515 -0.261065 0.164758 -0.204567 0.111747 0.224852 0.095345 -0.165600 -0.098104 0.185224 -1.007427 0.383425 -0.844146 0.595892 0.682144 0.536070 -0.048185 0.385394 0.886149 -0.180272 0.679478 -0.272291 -0.061443 0.372345 -0.357528 0.065238 -0.265427 -0.110998 -0.033170 0.184583 -0.193668 0.258645 -0.214488 -0.135857 0.304798 -0.146043 -0.552069 0.131506 0.037543 0.080747 0.025523 0.064832 -0.037481 0.065358 -0.192135 -0.066773 0.075565 -0.007293 0.009434 -0.045009 0.019042 0.000184 -0.124571 0.022683 -0.001122 0.007883 0.041079 0.061792 0.043677 0.007882 -0.082375 0.037232 0.003298 0.007174 0.061770 0.077339 -0.079842 -0.007876 -0.000433 0.000739 -0.005631 0.004462 -0.009006 -0.000520 0.000470 -0.001306 -0.000481 -0.000103 6.595787e-14 5.572357e-13 1.493610e-13 1.861709e-13 -1.458207e-13 1.938398e-13 4.905571e-13 -3.348726e-13 -4.624119e-13 4.307896e-13 7.588101e-13 -1.736184e-13 5.680962e-13 -2.892674e-13 2.425362e-13 2.819889e-13 -6.391905e-13 -2.246249e-13 -2.430504e-13 -1.775977e-13 7.003689e-14 3.942614e-13 -1.315704e-13 -4.112645e-13 4.396000e-13 4.707710e-13 -1.988568e-13 7.566560e-14 -1.218159e-15 0.000304 0.000227 0.001718 0.003683 -0.006668 0.013104 0.004652 0.007286 0.011800 -0.000609 -0.000471 0.009785 -0.008856 -0.051621 0.013161 0.068941 0.047872 0.036074 -0.053992 -0.160231 0.041248 -0.108446 -0.006411 0.048080 -0.053402 -0.053870 -0.204285 0.153997 0.013815 -0.113353 -0.039425 -0.145924 -0.333918 0.043463 -0.278368 -0.310839 -0.138256 -0.042843 0.353198 -0.252925 -0.081981 -0.385320 -0.021495 0.280847 0.318372 -0.507378 0.214363 -0.423207 -0.486062 -0.073222 -0.583009 -0.064513 -0.660985 0.708667 -0.263698 0.221439 0.245657 -0.499414 -0.009785 0.005127 0.424804 -0.511147 -0.067987 -0.069496 -0.111682 -0.021870 0.185835 0.187878 3.724114 3.534396 1.997752 9.864420 3.108090 3.489672 -0.597693 -0.891177 -1.024769 1.620283 1.620283 1.620283 1.757292 1.779978 1.779978 1.779978 1.757292 1.764859 1.764859 1.764859 1.684202 1.454577 1.454577 1.454577 1.684202 0.696360 0.470696 0.470696 0.470696 0.696360 0.477217 -0.114632 -0.114632 -0.114632 0.477217 1.593650 2.122842 0.558238 1.722099 2.322322 2.148563 2.219609 2.525239 1.649855 2.195268 1.968594 1.725175 2.200811 3.569658 1.417214 1.423639 -0.345984 1.118643 -1.299069 1.811528 0.007543 -0.516916 1.999208 2.375083 2.066492 3.091316 -1.018980 -1.093911 -0.594753 -0.430420 -0.437206 -0.074659 -0.081536 -0.318073
3 -0.549461 0.549461 -0.243320 -0.200086 -0.460142 -0.157398 -0.043311 -0.159226 -0.320339 -0.221602 0.922535 -0.007421 -0.007421 -0.007421 1.352711 -0.007421 -0.007421 -1.183518 -0.007421 -0.007421 -0.258813 -1.613048 2.113955 0.781513 -0.041090 0.284282 -0.038338 -0.676021 -1.427084 -0.080922 3.231922 1.295412 0.012508 0.332316 0.626892 0.607942 0.036492 0.229525 0.042520 0.390681 0.062942 -0.044912 0.096292 0.095894 -0.017115 0.124980 -0.027133 0.025587 -0.036045 0.104323 0.062240 -0.069196 0.049094 -0.004196 0.075716 0.044748 -0.003177 -0.075337 -0.000055 0.105245 -0.048945 -0.061112 -0.038348 -0.031764 -0.054545 -0.057354 -0.121143 0.013155 -0.007405 -0.030269 0.047155 -0.016913 0.092906 -0.039380 -0.077470 -0.082177 -0.037030 0.173840 -0.171706 -0.016847 0.016145 -0.062745 -0.022843 -0.015480 0.027843 -0.041203 0.003861 -0.008904 -0.046435 -0.061257 -0.046170 -0.038983 -0.051279 -0.072083 0.056905 -0.002039 0.001940 0.039230 0.003453 0.304404 1.413800 -0.526698 -0.375402 0.109597 -0.423688 -0.035690 -0.089234 -0.456109 0.205518 -0.009879 -0.199800 -0.843242 -1.662875 -1.626516 0.353621 -3.060685 -1.887535 -3.938735 -0.084146 0.777366 -1.160367 -2.086786 -3.793884 2.372960 17.346957 0.323497 7.185653 1.716609 0.493613 -7.657147 4.487332 5.395448 -2.494822 -2.274507 0.867822 0.654820 0.814867 -0.011625 -0.329482 0.196535 0.304050 -0.063660 1.023886 0.354228 -0.874284 0.076024 -0.260614 -0.520658 -0.654328 -0.061348 0.259709 0.510558 0.742844 -0.396823 -0.282553 0.314588 0.097260 0.054746 0.129444 0.039219 0.011476 0.229740 0.347145 0.139064 -0.038718 0.089252 -0.076511 0.001235 0.034633 0.043739 -0.017059 -0.046728 -0.018559 0.000869 -0.025033 -0.050967 -0.008663 0.032284 0.000171 0.031409 0.016561 0.008224 0.010151 -0.008566 0.015066 -0.008863 0.066714 -0.040589 -0.009727 -0.005400 -0.036307 -0.000109 -0.020808 -0.018713 -0.001896 0.014031 0.010852 -0.018255 0.048484 0.018804 0.008596 0.004784 0.020417 0.015194 -0.033876 -0.016839 -0.025777 -0.005566 -0.041549 -0.031028 -0.016025 -0.038980 -0.030797 0.020078 0.012062 0.040959 -0.008658 0.011459 0.007490 0.026479 -0.189486 -0.210200 0.011898 -0.014409 -0.101808 0.075313 0.206237 -0.077441 0.201667 0.069879 0.079310 0.142394 -0.192540 -0.021895 -0.111482 -0.326456 -0.042605 -0.208090 0.107562 0.066910 -0.273328 0.050957 0.069328 0.074669 0.395907 0.004283 0.162800 -0.221290 -0.041560 0.189613 0.257747 -0.024757 0.250510 -0.319735 0.076783 -0.275395 -0.132746 0.117053 0.066243 -0.116763 -0.027526 -0.214193 0.040811 0.055356 -0.026428 0.032285 -0.048165 -0.038001 -0.126100 0.107319 -0.030829 0.126672 0.056959 -0.029922 0.036021 -0.045177 -0.014616 -0.149680 0.040414 -0.034874 0.059327 -0.114990 0.084580 0.004520 -0.010172 0.184346 0.256402 -0.080829 0.032665 0.135759 0.021214 0.000104 0.102261 0.143784 0.051508 0.020533 -0.081731 0.155202 0.086739 0.155264 -0.093700 0.038151 0.104730 0.023600 0.023212 0.000399 0.017778 0.085027 -0.009863 0.095550 -0.040690 -0.044875 0.051347 0.019507 -0.064203 0.041495 -0.046404 -0.048103 0.013467 -0.002091 -0.051455 0.028990 0.022104 -0.008755 0.020917 0.001462 0.024211 -0.014315 -0.020055 -0.005578 0.023377 -0.003179 0.013755 -0.003950 0.009567 -0.010010 -0.003680 0.003423 0.004497 -0.001572 -0.001222 -0.004080 0.010599 -0.009886 -0.001997 -0.008885 0.016906 0.004692 0.004702 -0.000350 -0.013489 0.004569 0.001811 0.001931 -0.000296 0.007719 0.000198 -0.000149 0.000294 0.000246 -0.001287 -0.000022 5.403858e-13 1.871688e-12 -1.848559e-14 7.747310e-13 1.130374e-13 -3.566032e-13 -2.856897e-14 3.142888e-13 -2.752260e-13 3.778185e-13 5.790726e-13 2.552148e-13 3.743538e-14 1.696583e-13 -2.320552e-13 1.695277e-13 -5.041000e-13 6.517461e-13 6.850483e-14 3.872802e-13 1.045042e-13 4.540865e-13 3.039817e-13 -5.029946e-13 2.280004e-13 1.057661e-12 -1.686482e-14 4.138114e-13 4.350187e-13 0.000898 0.000064 -0.000592 0.000722 0.002130 -0.002865 -0.007820 0.002377 0.002385 -0.004110 -0.000110 -0.015326 0.006797 -0.066191 -0.010088 -0.001763 0.003451 0.035864 -0.024105 -0.026619 -0.001218 -0.039864 0.041715 0.013126 -0.074898 -0.036987 0.048937 -0.004382 -0.156708 0.005329 0.001246 0.125010 0.064376 -0.006293 -0.047094 -0.097946 0.021965 -0.097069 0.010510 -0.028651 0.024781 0.071788 0.036134 -0.135334 -0.066450 -0.091750 0.070086 -0.055203 0.005229 0.006194 -0.154561 -0.169844 0.041748 -0.063338 -0.202698 0.071541 0.020087 -0.091235 -0.146323 -0.156195 0.107056 -0.112310 0.218827 0.102852 0.125162 -0.151908 0.075588 0.401990 3.579489 3.534396 -0.465097 7.326477 0.079797 -1.800331 -1.161957 1.750741 0.129602 -2.545731 -2.545731 -2.545731 -2.517663 -2.516977 -2.516977 -2.516977 -2.517663 -2.522831 -2.522831 -2.522831 -2.554472 -2.565223 -2.565223 -2.565223 -2.554472 -2.551047 -2.486788 -2.486788 -2.486788 -2.551047 -2.516127 -2.382176 -2.382176 -2.382176 -2.516127 -1.782517 -1.667116 -1.800873 -0.590969 -1.690356 -1.976272 -1.422733 -1.365303 -0.111691 -0.981022 -0.510075 -0.459193 -0.237281 3.125717 -1.483228 -1.419003 0.161642 -1.272151 -0.104484 -1.822948 -1.028876 -0.806823 -1.943748 1.031994 -0.544371 0.817758 -1.621878 -1.232393 -1.534226 4.147180 4.058124 4.288340 4.203960 4.304691
4 -0.549461 0.549461 4.109822 -0.200086 -0.460142 -0.157398 -0.043311 -0.159226 -0.320339 -0.221602 -1.083970 -0.007421 -0.007421 -0.007421 -0.739256 -0.007421 -0.007421 0.844939 -0.007421 -0.007421 -0.258813 -0.091062 -0.142138 -0.155704 -0.290690 0.018882 0.023538 0.088675 -0.259151 0.118454 -0.277223 0.113376 -0.030246 -0.012391 -0.296987 -0.305950 0.638791 -0.402836 0.095289 -0.677695 -0.323365 2.921820 -0.528324 -0.916167 0.784182 -2.881635 -4.046828 -1.403529 0.811813 -0.457896 -1.484107 0.336951 0.651645 -0.199662 -0.009815 -0.996969 1.291357 0.552010 -0.001381 0.759154 6.402136 0.014643 -4.568361 -0.669343 -0.688380 -0.916656 -0.460104 -0.289265 -1.162298 0.393361 0.214624 0.007462 0.046069 -0.162350 -0.066949 0.125466 0.056490 -0.144479 -0.154396 -0.238182 0.140208 -0.092793 0.090896 0.107455 -0.228375 -0.123853 -0.045649 -0.096664 0.029720 -0.013703 0.025540 0.009465 0.436134 -0.207360 0.023093 -0.133724 -0.282796 -0.306715 0.035326 -0.189899 -0.043495 -0.135987 0.025136 -0.120619 -0.229064 0.199400 0.396874 0.200408 -0.227465 0.042851 -0.035421 10.683493 2.498634 1.421645 -1.076819 -0.811425 0.223074 5.204287 -3.543576 2.357343 11.674996 0.966778 4.815652 -5.562447 -2.146573 6.493908 5.744070 2.263234 -4.018663 -3.833655 3.351597 1.992660 -4.387845 -1.353734 -1.154061 0.345595 0.172170 -1.123522 -2.063017 0.341783 -0.902381 1.248841 -1.009345 0.932496 0.776984 -0.960601 0.800480 -0.117982 0.075546 0.405736 0.643886 0.206377 0.284722 0.039798 -0.485481 0.322969 0.596088 -0.173772 0.133415 1.125293 -0.347591 0.702950 1.358908 -1.904851 0.079878 0.238077 0.186989 0.109801 -0.030554 -0.035435 -0.126706 0.032382 -0.081465 -0.148267 0.075632 -0.023674 -0.059222 -0.016287 0.092370 0.035201 -0.026172 0.028214 0.001353 0.041429 0.003150 0.006559 0.030333 -0.001168 -0.043496 -0.025036 -0.011054 0.002166 -0.053098 0.008663 -0.005129 0.035345 -0.014254 0.009067 0.044906 -0.047469 -0.021653 0.000397 0.043235 -0.007156 0.037646 0.010363 -0.014186 -0.039908 -0.047054 -0.052000 -0.045913 0.079196 -0.028701 0.007140 0.000791 0.023369 0.026717 0.047993 0.052964 0.110131 -0.125628 -0.037942 -0.108221 0.018023 0.124822 0.097621 0.147474 0.097956 -0.015887 -0.138383 -0.070688 0.060297 -0.094765 -0.372871 -0.104256 0.055191 -0.089231 0.032969 0.175892 0.080074 -0.149253 0.041967 -0.336697 -0.024249 0.127932 0.011851 0.072007 -0.212062 -0.208177 -0.339806 0.279147 0.051760 0.237822 0.077641 -0.006038 0.215633 -0.205767 -0.107031 -0.076876 -0.051448 -0.120190 -0.071160 -0.042202 -0.022066 0.015799 0.113661 -0.034110 0.122412 -0.004128 -0.104020 0.001683 -0.034192 -0.031496 0.007867 -0.047550 -0.056216 0.097000 -0.136182 0.017754 0.108677 0.106493 0.039514 -0.066111 0.057269 0.182240 0.054177 0.086463 0.053867 -0.017969 -0.020106 0.024826 0.116873 -0.211760 -0.111568 0.044271 -0.124540 0.123316 0.064723 0.190813 -0.132186 -0.133371 -0.377998 0.065018 0.027507 -0.624117 -0.023784 0.528061 0.033043 0.190279 -0.029297 0.039950 -0.169085 0.037868 0.319105 -0.066247 -0.140469 -0.144764 0.279565 -0.095480 -0.180349 -0.136198 0.067375 0.200282 0.035690 0.074034 0.032853 -0.002653 0.128821 -0.104578 -0.012650 0.026667 0.030600 0.026710 -0.012186 0.000604 -0.044312 0.047991 -0.008702 -0.000343 -0.037231 -0.045570 0.002996 0.022491 -0.013916 -0.018276 -0.004874 -0.000790 0.006506 -0.006051 -0.007285 0.006555 0.004848 -0.002896 -0.010982 0.020604 -0.006821 -0.006159 -0.001374 -0.000871 -0.000553 -0.001962 0.001128 8.134414e-13 3.343869e-12 3.403775e-13 1.247558e-12 -5.078834e-13 -7.881740e-13 2.150963e-13 3.019587e-13 -1.342819e-12 8.734203e-13 1.534630e-12 -1.445148e-13 1.707971e-14 -7.835449e-14 2.055861e-13 -5.659225e-13 -8.610126e-13 1.877626e-13 -1.848306e-13 5.592423e-13 4.748970e-14 4.573081e-13 4.096153e-13 -3.742568e-13 -1.916247e-13 9.886954e-13 -8.823566e-14 4.101260e-13 8.240556e-13 0.001114 0.001579 0.003688 -0.000778 0.000855 0.009859 -0.002172 0.006321 0.018739 -0.008307 -0.001476 -0.017744 0.068083 -0.003997 -0.035762 0.049756 0.022789 0.009348 -0.048196 -0.012751 0.049627 0.074143 0.044665 0.040591 -0.001128 -0.020650 0.091526 -0.162773 -0.043332 0.041396 -0.008670 0.013198 0.135069 0.188542 -0.064135 0.108226 0.198994 -0.105229 -0.055194 0.036052 -0.393483 0.024604 -0.077289 -0.061464 -0.079109 -0.056735 -0.124512 0.146562 0.023865 0.033096 -0.099578 -0.150772 0.081821 0.116195 0.095288 0.114041 0.259925 -0.158098 -0.201357 0.106939 0.300085 0.046780 -0.287300 -0.043393 0.042920 -0.013065 0.006991 0.401990 3.579489 3.371377 2.503515 7.326477 3.108090 2.167171 -0.785781 -0.136343 -0.768242 1.521092 1.521092 1.521092 1.661226 1.683417 1.683417 1.683417 1.661226 1.716682 1.716682 1.716682 1.684202 1.752340 1.752340 1.752340 1.684202 1.295881 1.308650 1.308650 1.308650 1.295881 1.125774 0.802035 0.802035 0.802035 1.125774 2.356010 1.866764 0.155463 2.266350 2.209288 1.619738 2.056519 2.296384 2.498007 1.955548 0.896737 0.769514 1.049490 3.236703 0.922017 2.061783 -0.176775 1.684884 0.771546 2.278818 1.159120 0.691027 1.896793 2.869906 1.939132 2.566649 1.040923 0.475556 0.250772 -0.091339 -0.200610 -0.680632 -0.375063 -0.206681

Data Boxplot

In [39]:
def get_boxpolt_info(data, size=[10,5], axis_rotation=0):
    plt.rcParams['figure.figsize'] = size
    ax = sns.boxplot(data=data)
    plt.title('Numerical Features Distribution')
    if axis_rotation:
        plt.xticks(rotation=axis_rotation)
    plt.show()
In [40]:
get_boxpolt_info(df_ordinal[num_col_names], size=(20, 5), axis_rotation=90)
In [41]:
get_boxpolt_info(df_ordinal[cat_col_names], size=(20, 5), axis_rotation=90)
In [53]:
df_ordinal.reset_index(inplace=True, drop=True)
df_dummies.reset_index(inplace=True, drop=True)
In [54]:
label.reset_index(inplace=True, drop=True)
In [ ]:
df_ordinal_final = df_ordinal.join(label)
df_ordinal_final.to_csv('./data/processed/1_1_processed_ordinal_encoding.csv', index_label=False)
In [55]:
df_dummies_final = df_dummies.join(label)
df_dummies_final.to_csv('../data/processed/1_1_processed_onehot_encoding.csv', index_label=False)